ELT / ETL pipeline development
ELT and ETL pipeline development is the engineering discipline of designing, building, and maintaining the processes that extract data from source systems, transform it into a usable format, and load it into target environments for analytics, operations, and reporting. Solvaria’s senior engineers build pipelines that are reliable, auditable, and built to scale—handling the complexity of enterprise data environments without the fragility that comes from rushed or undocumented implementations.
When unreliable pipelines undermine your data
Poorly built pipelines are one of the most common sources of data quality problems in enterprise environments. Transformations applied inconsistently, loads that fail silently, undocumented logic that no one can maintain—these issues compound over time, eroding trust in reporting and creating hidden risk in downstream analytics and AI workloads. As data volumes grow and source systems change, fragile pipelines break in ways that are difficult to diagnose and expensive to fix.
Solvaria’s approach to pipeline development
We design and build ELT and ETL pipelines that are engineered for durability, not just delivery. Our team documents transformation logic, builds in error handling and alerting, and structures pipelines to be maintainable by your team after handoff. We work with your existing tooling where possible and recommend modern alternatives when legacy approaches are creating unnecessary complexity.
Engagements typically begin with an audit of existing pipelines—identifying failures, bottlenecks, and undocumented logic—before design and build begin. For greenfield implementations, we work from your source system inventory and analytics requirements to design a pipeline architecture that scales with your data strategy.
Core capabilities
Pipeline architecture and design
Design scalable pipeline frameworks that account for data volume, source system variability, transformation complexity, and target environment requirements.
ETL development
Build extract, transform, and load pipelines that apply business logic before loading, supporting use cases where transformation must occur prior to storage.
ELT development
Implement extract, load, and transform pipelines that leverage the compute power of modern cloud warehouses to apply transformations post-load, improving performance and flexibility.
Incremental and real-time pipeline patterns
Design pipelines that process only new or changed data, reducing load times and infrastructure costs while supporting near-real-time analytics use cases.
Error handling and monitoring
Implement alerting, retry logic, and audit logging so that pipeline failures surface immediately and root causes are traceable.
Pipeline testing and validation
Build automated tests that validate transformation logic, data completeness, and load accuracy before pipelines reach production.
Legacy pipeline modernization
Audit, refactor, and migrate legacy ETL processes built on aging tooling to modern, maintainable frameworks without disrupting downstream consumers.
Technologies and environments we support
Our pipeline engineers bring hands-on experience across the full range of modern and legacy data integration tooling, including Azure Data Factory, Databricks, dbt, Apache Airflow, and AWS Glue. We connect pipelines to source systems including SQL Server, Oracle, PostgreSQL, MySQL, and major SaaS platforms, and deliver data to Snowflake, Azure Synapse, Redshift, and on-premises targets. Our onshore, U.S.-based team also modernizes legacy implementations built on SSIS, Informatica, and Talend — preserving business logic while eliminating the technical debt that makes older pipelines expensive to maintain.
Related services
Cloud data engineering
Design and manage scalable cloud data pipelines and warehouses across AWS, Azure, and multi-cloud environments.
Data repository engineering
Build structured data repositories—warehouses, marts, and operational stores—that serve as reliable destinations for your pipeline output.
Azure Data Factory
Design, build, and manage Azure Data Factory pipelines for enterprise-scale data integration on the Microsoft platform.
Databricks
Leverage Databricks for advanced pipeline development, large-scale transformations, and unified analytics workloads.
Let’s talk about your data pipelines
Engage our team to review your current pipeline environment and identify where engineering improvements can improve reliability, reduce maintenance burden, and support your analytics roadmap.